Systems Biology for Drug Target Discovery in Acute Myeloid Leukemia.

Combining new therapeutics with all-trans-retinoic acid (ATRA) could improve the efficiency of acute myeloid leukemia (AML) treatment. Modeling the process of ATRA-induced differentiation based on the transcriptomic profile of leukemic cells resulted in the identification of key targets that can be used to increase the therapeutic effect of ATRA. The genome-scale transcriptome analysis revealed the early molecular response to the ATRA treatment of HL-60 cells. In this study, we performed the transcriptomic profiling of HL-60, NB4, and K562 cells exposed to ATRA for 3-72 h. After treatment with ATRA for 3, 12, 24, and 72 h, we found 222, 391, 359, and 1032 differentially expressed genes (DEGs) in HL-60 cells, as well as 641, 1037, 1011, and 1499 DEGs in NB4 cells. We also found 538 and 119 DEGs in K562 cells treated with ATRA for 24 h and 72 h, respectively. Based on experimental transcriptomic data, we performed hierarchical modeling and determined cyclin-dependent kinase 6 (CDK6), tumor necrosis factor alpha (TNF-alpha), and transcriptional repressor CUX1 as the key regulators of the molecular response to the ATRA treatment in HL-60, NB4, and K562 cell lines, respectively. Mapping the data of TMT-based mass-spectrometric profiling on the modeling schemes, we determined CDK6 expression at the proteome level and its down-regulation at the transcriptome and proteome levels in cells treated with ATRA for 72 h. The combination of therapy with a CDK6 inhibitor (palbociclib) and ATRA (tretinoin) could be an alternative approach for the treatment of acute myeloid leukemia (AML).

Loss of mutant p53 in HaCaT keratinocytes promotes cadmium-induced keratin 17 expression and cell death.

BACKGROUND: Cadmium exposure induces dermatotoxicity and epidermal barrier disruption and leads to the development of various pathologies. HaCaT cells are immortalized human keratinocytes that are widely used as alternatives to primary human keratinocytes, particularly for evaluating cadmium toxicity. HaCaT cells bear two gain-of-function (GOF) mutations in the TP53 gene, which strongly affect p53 function. Mutant forms of p53 are known to correlate with increased resistance to various stimuli, including exposure to cytotoxic substances. In addition, keratin 17 (KRT17) was recently shown to be highly expressed in HaCaT cells in response to genotoxic stress. Moreover, p53 is a direct transcriptional repressor of KRT17. However, the impact of TP53 mutations in HaCaT cells on the regulation of cell death and keratin 17 expression is unclear. In this study, we aimed to evaluate the impact of p53 on the response to Cd-induced cytotoxicity. METHODS AND RESULTS: Employing the MTT assay and Annexin V/propidium iodide staining, we demonstrated that knockout of TP53 leads to a decrease in the sensitivity of HaCaT cells to the cytotoxic effects of cadmium. Specifically, HaCaT cells with TP53 knockout (TP53 KO HaCaT) exhibited cell death at a cadmium concentration of 10 µM or higher, whereas wild-type cells displayed cell death at a concentration of 30 µM. Furthermore, apoptotic cells were consistently detected in TP53 KO HaCaT cells upon exposure to low concentrations of cadmium (10 and 20 µM) but not in wild-type cells. Our findings also indicate that cadmium cytotoxicity is mediated by reactive oxygen species (ROS), which were significantly increased only in TP53 knockout cells treated with 30 µM cadmium. An examination of proteomic data revealed that TP53 knockout in HaCaT cells resulted in the upregulation of proteins involved in the regulation of apoptosis, redox systems, and DNA repair. Moreover, RT‒qPCR and immunoblotting showed that cadmium toxicity leads to dose-dependent induction of keratin 17 in p53-deficient cells but not in wild-type cells. CONCLUSIONS: The connection between mutant p53 in HaCaT keratinocytes and increased resistance to cadmium toxicity was demonstrated for the first time. Proteomic profiling revealed that TP53 knockout in HaCaT cells led to the activation of apoptosis regulatory circuits, redox systems, and DNA repair. In addition, our data support the involvement of keratin 17 in the regulation of DNA repair and cell death. Apparently, the induction of keratin 17 is p53-independent but may be inhibited by mutant p53.

Exploring the Functions of Mutant p53 through TP53 Knockout in HaCaT Keratinocytes.

Approximately 50% of tumors carry mutations in TP53; thus, evaluation of the features of mutant p53 is crucial to understanding the mechanisms underlying cell transformation and tumor progression. HaCaT keratinocytes represent a valuable model for research in this area since they are considered normal, although they bear two gain-of-function mutations in TP53. In the present study, transcriptomic and proteomic profiling were employed to examine the functions of mutant p53 and to investigate the impact of its complete abolishment. Our findings indicate that CRISPR-mediated TP53 knockout results in significant changes at the transcriptomic and proteomic levels. The knockout of TP53 significantly increased the migration rate and altered the expression of genes associated with invasion, migration, and EMT but suppressed the epidermal differentiation program. These outcomes suggest that, despite being dysfunctional, p53 may still possess oncosuppressive functions. However, despite being considered normal keratinocytes, HaCaT cells exhibit oncogenic properties.

Evaluation of Cd-induced cytotoxicity in primary human keratinocytes.

An increasing number of studies have investigated the effects of Cd on human health. Cd-induced dermatotoxicity is an important field of research, but numerous studies have focused on the effects of Cd on the human skin. Moreover, most studies have been performed using HaCaT cells but not primary keratinocytes. In this study, we provide the results describing the cytotoxic effects of Cd exposure on primary human epidermal keratinocytes obtained from different donors. The subtoxic concentration of cadmium chloride was determined via MTT assay, and transcriptomic analysis of the cells exposed to this concentration (25 µM) was performed. As in HaCaT cells, Cd exposure resulted in increased ROS levels, cell cycle arrest, and induction of apoptosis. In addition, we report that exposure to Cd affects zinc and copper homeostasis, induces metallothionein expression, and activates various signaling pathways, including Nrf2, NF-kB, TRAIL, and PI3K. Cd induces the secretion of various cytokines (IL-1, IL-6, IL-10, and PGE2) and upregulates the expression of several cytokeratins, such as KRT6B, KRT6C, KRT16, and KRT17. The results provide a better understanding of the mechanisms of cadmium-induced cytotoxicity and its effect on human epidermal skin cells.

The effect of TLR3 priming conditions on MSC immunosuppressive properties.

BACKGROUND: Mesenchymal stromal cells (MSCs) have regenerative and immunomodulatory properties, making them suitable for cell therapy. Toll-like receptors (TLRs) in MSCs respond to viral load by secreting immunosuppressive or proinflammatory molecules. The expression of anti-inflammatory molecules in MSCs can be altered by the concentration and duration of exposure to the TLR3 ligand polyinosinic-polycytidylic acid (poly(I:C)). This study aimed to optimize the preconditioning of MSCs with poly(I:C) to increase immunosuppressive effects and to identify MSCs with activated TLR3 (prMSCs). METHODS: Flow cytometry and histochemical staining were used to analyze MSCs for immunophenotype and differentiation potential. MSCs were exposed to poly(I:C) at 1 and 10 µg/mL for 1, 3, and 24 h, followed by determination of the expression of IDO1, WARS1, PD-L1, TSG-6, and PTGES2 and PGE2 secretion. MSCs and prMSCs were cocultured with intact (J-) and activated (J+) Jurkat T cells. The proportion of proliferating and apoptotic J+ and J- cells, IL-10 secretion, and IL-2 production after cocultivation with MSCs and prMSCs were measured. Liquid chromatography-mass spectrometry and bioinformatics analysis identified proteins linked to TLR3 activation in MSCs. RESULTS: Poly(I:C) at 10 µg/mL during a 3-h incubation caused the highest expression of immunosuppression markers in MSCs. Activation of prMSCs caused a 18% decrease in proliferation and a one-third increase in apoptotic J+ cells compared to intact MSCs. Cocultures of prMSCs and Jurkat cells had increased IL-10 and decreased IL-2 in the conditioned medium. A proteomic study of MSCs and prMSCs identified 53 proteins with altered expression. Filtering the dataset with Gene Ontology and Reactome Pathway revealed that poly(I:C)-induced proteins activate the antiviral response. Protein‒protein interactions by String in prMSCs revealed that the antiviral response and IFN I signaling circuits were more active than in native MSCs. prMSCs expressed more cell adhesion proteins (ICAM-I and Galectin-3), PARP14, PSMB8, USP18, and GBP4, which may explain their anti-inflammatory effects on Jurkat cells. CONCLUSIONS: TLR3 activation in MSCs is dependent on exposure time and poly(I:C) concentration. The maximum expression of immunosuppressive molecules was observed with 10 µg/mL poly(I:C) for 3-h preconditioning. This priming protocol for MSCs enhances the immunosuppressive effects of prMSCs on T cells.

Cytokine Profile in Lung Cancer Patients: Anti-Tumor and Oncogenic Cytokines.

Lung cancer is currently the second leading cause of cancer death worldwide. In recent years, checkpoint inhibitor immunotherapy (ICI) has emerged as a new treatment. A better understanding of the tumor microenvironment (TMJ) or the immune system surrounding the tumor is needed. Cytokines are small proteins that carry messages between cells and are known to play an important role in the body's response to inflammation and infection. Cytokines are important for immunity in lung cancer. They promote tumor growth (oncogenic cytokines) or inhibit tumor growth (anti-tumour cytokines) by controlling signaling pathways for growth, proliferation, metastasis, and apoptosis. The immune system relies heavily on cytokines. They can also be produced in the laboratory for therapeutic use. Cytokine therapy helps the immune system to stop the growth or kill cancer cells. Interleukins and interferons are the two types of cytokines used to treat cancer. This article begins by addressing the role of the TMJ and its components in lung cancer. This review also highlights the functions of various cytokines such as interleukins (IL), transforming growth factor (TGF), and tumor necrosis factor (TNF).

Angiogenesis in Lung Cancer: Understanding the Roles of Growth Factors.

Research has shown the role of growth factors in lung cancer angiogenesis. Angiogenesis promotes lung cancer progression by stimulating tumor growth, enhancing tumor invasion, contributing to metastasis, and modifying immune system responses within the tumor microenvironment. As a result, new treatment techniques based on the anti-angiogenic characteristics of compounds have been developed. These compounds selectively block the growth factors themselves, their receptors, or the downstream signaling pathways activated by these growth factors. The EGF and VEGF families are the primary targets in this approach, and several studies are being conducted to propose anti-angiogenic drugs that are increasingly suitable for the treatment of lung cancer, either as monotherapy or as combined therapy. The efficacy of the results are encouraging, but caution must be placed on the higher risk of toxicity, outlining the importance of personalized follow-up in the management of these patients.

Comparative proteoinformatics revealed the essentials of SDS impact on HaCaT keratinocytes.

There is no direct evidence supporting that SDS is a carcinogen, so to investigate this fact, we used HaCaT keratinocytes as a model of human epidermal cells. To reveal the candidate proteins and/or pathways characterizing the SDS impact on HaCaT, we proposed comparative proteoinformatics pipeline. For protein extraction, the performance of two sample preparation protocols was assessed: 0.2% SDS-based solubilization combined with the 1DE-gel concentration (Protocol 1) and osmotic shock (Protocol 2). As a result, in SDS-exposed HaCaT cells, Protocol 1 revealed 54 differentially expressed proteins (DEPs) involved in the disease of cellular proliferation (DOID:14566), whereas Protocol 2 found 45 DEPs of the same disease ID. The 'skin cancer' term was a single significant COSMIC term for Protocol 1 DEPs, including those involved in double-strand break repair pathway (BIR, GO:0000727). Considerable upregulation of BIR-associated proteins MCM3, MCM6, and MCM7 was detected. The eightfold increase in MCM6 level was verified by reverse transcription qPCR. Thus, Protocol 1 demonstrated high effectiveness in terms of the total number and sensitivity of MS identifications in HaCaT cell line proteomic analysis. The utility of Protocol 1 was confirmed by the revealed upregulation of cancer-associated MCM6 in HaCaT keratinocytes induced by non-toxic concentration of SDS. Data are available via ProteomeXchange with identifier PXD035202.

Nuclear Proteomics of Induced Leukemia Cell Differentiation.

Studies of induced granulocytic differentiation help to reveal molecular mechanisms of cell maturation. The nuclear proteome represents a rich source of regulatory molecules, including transcription factors (TFs). It is important to have an understanding of molecular perturbations at the early stages of the differentiation processes. By applying the proteomic quantitative profiling using isobaric labeling, we found that the contents of 214, 319, 376, 426, and 391 proteins were altered at 3, 6, 9, 12, and 72 h, respectively, compared to 0 h in the HL-60 cell nuclear fraction under all-trans-retinoid acid (ATRA) treatment. From 1860 identified nuclear proteins, 231 proteins were annotated as proteins with transcription factor (TF) activity. Six TFs (RREB1, SRCAP, CCDC124, TRIM24, BRD7, and BUD31) were downregulated and three TFs EWSR1, ENO1, and FUS were upregulated at early time points (3-12 h) after ATRA treatment. Bioinformatic annotation indicates involvement of the HL-60 nuclear proteome in DNA damage recognition in the RUNX1-triggered pathway, and in the p53-regulation pathway. By applying scheduled multiple reaction monitoring using stable isotopically labeled peptide standards (MRM/SIS), we found a persistent increase in the content of the following proteins: PRAM1, CEPBP, RBPJ, and HIC1 in the HL-60 cell nuclear fraction during ATRA-induced granulocytic differentiation. In the case of STAT1, CASP3, PARP1, and PRKDC proteins, a transient increase in their content was observed at early time points (3-12 h) after the ATRA treatment. Obtained data on nuclear proteome composition and dynamics during granulocytic differentiation could be beneficial for the development of new treatment approaches for leukemias with the mutated p53 gene.

Impact of p53 knockdown on protein dataset of HaCaT cells.

The HaCaT line of immortalized non-tumor cells is a popular model of keratinocytes used for dermatological studies, in the practice of toxicological tests, and in the study of skin allergic reactions. These cells maintain a stable keratinocyte phenotype, do not require specific growth factors during cultivation, and respond to keratinocyte differentiation stimuli. HaCaT cells bear two mutant p53 alleles - R282Q and H179Y. At least two mechanisms of GOF (gain-of-function) of mutant p53 are known: it affects functions of p63/p73 by inhibiting their binding to DNA; or it binds to new DNA sites by interacting with other transcription factors (NF-Y, E2F1, NF-KB, VDR, p63). Proteins of the P53 family play an important role in the regulation of proliferation and differentiation processes of human keratinocytes. Proteomic study of HaCaT cells with TP53 gene knockdown provides new data for understanding the limitations of HaCaT cells when using them as an experimental model of normal human keratinocytes. In this article we present datasets obtained through the high-throughput shotgun proteomics analysis of human immortalized HaCaT keratinocytes and p53 knockdown HaCaT keratinocytes. As a protocol for proteomic profiling of cells, we used the approach of obtaining LC-MS/MS measurements followed by their processing with MaxQuant software (version 1.6.3.4). The "RAW" files were deposited to the ProteomeXchange with identifier PXD033538.

Relative importance of climate and spatial processes in shaping species composition, functional structure and beta diversity of phytoplankton in a large river.

Although metacommunity dynamics of lentic phytoplankton are relatively well-documented, studies on the role of environmental and spatial processes in shaping phytoplankton communities of large rivers are still scarce. Here, we examined six phytoplankton data sets, which were collected in 1978-2017 from large river-scale segments (mean spatial extent 1117 km) in the Danube River. Our aim was to elucidate role of climatic, spatial and temporal predictors in variation of phytoplankton beta diversity using variance partitioning for compositions of species and functional groups sensu Reynolds. We hypothesised that phytoplankton beta diversity (measured as average distance to group centroid) would be positively related to both climatic heterogeneity and spatial extent used as a proxy for dispersal limitation. Additionally, we tested alternative dispersal models to evaluate different spatial processes structuring phytoplankton community. Our results revealed that spatial variables were more important than climatic factors in controlling both species and functional group composition. Climatic heterogeneity showed significant positive relationship with beta diversity. In contrast, there was no significant relationship between beta diversity and spatial extent, suggesting that spatial effect on beta-diversity was attenuated by anthropogenic disturbance. The better performance of non-directional model compared to model of water directionality suggested that spatial dynamics of phytoplankton metacommunity was in large part regulated by differences in the regional species pools. Spatial and temporal variables outperformed environmental (including climatic) factors in explaining phytoplankton metacommunity structure, indicating that phytoplankton exhibited strong biogeographical patterns. Thus, dispersal limitation interfered with species-sorting processes in determining phytoplankton community structure. In conclusion, our findings revealed that the development of a more reliable bioassessment program of the Danube River should be based on separation into basin regions.

Proteome Profiling of PMJ2-R and Primary Peritoneal Macrophages.

In vitro models are often used for studying macrophage functions, including the process of phagocytosis. The application of primary macrophages has limitations associated with the individual characteristics of animals, which can lead to insufficient standardization and higher variability of the obtained results. Immortalized cell lines do not have these disadvantages, but their responses to various signals can differ from those of the living organism. In the present study, a comparative proteomic analysis of immortalized PMJ2-R cell line and primary peritoneal macrophages isolated from C57BL/6 mice was performed. A total of 4005 proteins were identified, of which 797 were quantified. Obtained results indicate significant differences in the abundances of many proteins, including essential proteins associated with the process of phagocytosis, such as Elmo1, Gsn, Hspa8, Itgb1, Ncf2, Rac2, Rack1, Sirpa, Sod1, C3, and Msr1. These findings indicate that outcomes of studies utilizing PMJ2-R cells as a model of peritoneal macrophages should be carefully validated. All MS data are deposited in ProteomeXchange with the identifier PXD022133.

Trends in anesthesiology research: a machine learning approach to theme discovery and summarization.

OBJECTIVES: Traditionally, summarization of research themes and trends within a given discipline was accomplished by manual review of scientific works in the field. However, with the ushering in of the age of "big data," new methods for discovery of such information become necessary as traditional techniques become increasingly difficult to apply due to the exponential growth of document repositories. Our objectives are to develop a pipeline for unsupervised theme extraction and summarization of thematic trends in document repositories, and to test it by applying it to a specific domain. METHODS: To that end, we detail a pipeline, which utilizes machine learning and natural language processing for unsupervised theme extraction, and a novel method for summarization of thematic trends, and network mapping for visualization of thematic relations. We then apply this pipeline to a collection of anesthesiology abstracts. RESULTS: We demonstrate how this pipeline enables discovery of major themes and temporal trends in anesthesiology research and facilitates document classification and corpus exploration. DISCUSSION: The relation of prevalent topics and extracted trends to recent events in both anesthesiology, and healthcare in general, demonstrates the pipeline's utility. Furthermore, the agreement between the unsupervised thematic grouping and human-assigned classification validates the pipeline's accuracy and demonstrates another potential use. CONCLUSION: The described pipeline enables summarization and exploration of large document repositories, facilitates classification, aids in trend identification. A more robust and user-friendly interface will facilitate the expansion of this methodology to other domains. This will be the focus of future work for our group.

An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0.

Cohort identification for clinical studies tends to be laborious, time-consuming, and expensive. Developing automated or semi-automated methods for cohort identification is one of the "holy grails" in the field of biomedical informatics. We propose a high-throughput similarity-based cohort identification algorithm by applying numerical abstractions on Electronic Health Records (EHR) data. We implement this algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which enables sites using this standardized EHR data representation to avail this algorithm with minimum effort for local implementation. We validate its performance for a retrospective cohort identification task on six clinical trials conducted at the Columbia University Medical Center. Our algorithm achieves an average Area Under the Curve (AUC) of 0.966 and an average Precision at 5 of 0.983. This interoperable method promises to achieve efficient cohort identification in EHR databases. We discuss suitable applications of our method and its limitations and propose warranted future work.

EliIE: An open-source information extraction system for clinical trial eligibility criteria.

OBJECTIVE: To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0. MATERIALS AND METHODS: EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer's clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling-based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring. RESULTS: In task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation. CONCLUSIONS: This study presents EliIE, an OMOP CDM-based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.

Role of Microfluidics in Blood-Brain Barrier Permeability Cell Culture Modeling: Relevance to CNS Disorders.

In vitro modeling of the human blood-brain barrier (BBB) is critical for pre-clinical evaluation and predicting the permeability of newly developed potentially neurotoxic and neurotrophic drugs. Here we summarize the specific structural and functional features of endothelial cells as a key component of the BBB and compare analysis of different cell culture models in reflecting these features. Particular attention is paid to cellular models of the BBB in microfluidic devices capable of circulating nutrient media to simulate the blood flow of the brain. In these conditions, it is possible to reproduce a number of factors affecting endothelial cells under physiological conditions, including shear stress. In comparison with static cell models, concentration gradients, which determine the velocity of transport of substances, reproduce more accurately conditions of nutrient medium flow, since they eliminate the accumulation of substances near the basal membrane of cells, not typical for the situation in vivo. Co-cultivation of different types of cells forming the BBB, in separate cell chambers connected by microchannels, allows to evaluate the mutual influences of cells under normal conditions and when exposed to the test substance. New experimental possibilities that can be achieved through modeling of BBB in microfluidic devices determine the feasibility of their use in the practice for pre-clinical studies of novel drugs against neurodegenerative diseases.

Design, synthesis and biological evaluation of novel potent MDM2/p53 small-molecule inhibitors.

Regioselective synthesis, biological evaluation and 3D-molecular modeling for a series of novel diastereomeric 2-thioxo-5H-dispiro[imidazolidine-4,3-pyrrolidine-2,3-indole]-2,5(1H)-diones are described. The studied compounds have been tentatively identified as potent small molecule MDM2/p53 PPI inhibitors and can therefore be reasonably regarded as promising anticancer therapeutics.

Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research.

BACKGROUND: To demonstrate that subject selection based on sufficient laboratory results and medication orders in electronic health records can be biased towards sick patients. METHODS: Using electronic health record data from 10,000 patients who received anesthetic services at a major metropolitan tertiary care academic medical center, an affiliated hospital for women and children, and an affiliated urban primary care hospital, the correlation between patient health status and counts of days with laboratory results or medication orders, as indicated by the American Society of Anesthesiologists Physical Status Classification (ASA Class), was assessed with a Negative Binomial Regression model. RESULTS: Higher ASA Class was associated with more points of data: compared to ASA Class 1 patients, ASA Class 4 patients had 5.05 times the number of days with laboratory results and 6.85 times the number of days with medication orders, controlling for age, sex, emergency status, admission type, primary diagnosis, and procedure. CONCLUSIONS: Imposing data sufficiency requirements for subject selection allows researchers to minimize missing data when reusing electronic health records for research, but introduces a bias towards the selection of sicker patients. We demonstrated the relationship between patient health and quantity of data, which may result in a systematic bias towards the selection of sicker patients for research studies and limit the external validity of research conducted using electronic health record data. Additionally, we discovered other variables (i.e., admission status, age, emergency classification, procedure, and diagnosis) that independently affect data sufficiency.

Clustering clinical trials with similar eligibility criteria features.

OBJECTIVES: To automatically identify and cluster clinical trials with similar eligibility features. METHODS: Using the public repository ClinicalTrials.gov as the data source, we extracted semantic features from the eligibility criteria text of all clinical trials and constructed a trial-feature matrix. We calculated the pairwise similarities for all clinical trials based on their eligibility features. For all trials, by selecting one trial as the center each time, we identified trials whose similarities to the central trial were greater than or equal to a predefined threshold and constructed center-based clusters. Then we identified unique trial sets with distinctive trial membership compositions from center-based clusters by disregarding their structural information. RESULTS: From the 145,745 clinical trials on ClinicalTrials.gov, we extracted 5,508,491 semantic features. Of these, 459,936 were unique and 160,951 were shared by at least one pair of trials. Crowdsourcing the cluster evaluation using Amazon Mechanical Turk (MTurk), we identified the optimal similarity threshold, 0.9. Using this threshold, we generated 8806 center-based clusters. Evaluation of a sample of the clusters by MTurk resulted in a mean score 4.331±0.796 on a scale of 1-5 (5 indicating "strongly agree that the trials in the cluster are similar"). CONCLUSIONS: We contribute an automated approach to clustering clinical trials with similar eligibility features. This approach can be potentially useful for investigating knowledge reuse patterns in clinical trial eligibility criteria designs and for improving clinical trial recruitment. We also contribute an effective crowdsourcing method for evaluating informatics interventions.

From expert-derived user needs to user-perceived ease of use and usefulness: a two-phase mixed-methods evaluation framework.

Underspecified user needs and frequent lack of a gold standard reference are typical barriers to technology evaluation. To address this problem, this paper presents a two-phase evaluation framework involving usability experts (phase 1) and end-users (phase 2). In phase 1, a cross-system functionality alignment between expert-derived user needs and system functions was performed to inform the choice of "the best available" comparison system to enable a cognitive walkthrough in phase 1 and a comparative effectiveness evaluation in phase 2. During phase 2, five quantitative and qualitative evaluation methods are mixed to assess usability: time-motion analysis, software log, questionnaires - System Usability Scale and the Unified Theory of Acceptance of Use of Technology, think-aloud protocols, and unstructured interviews. Each method contributes data for a unique measure (e.g., time motion analysis contributes task-completion-time; software log contributes action transition frequency). The measures are triangulated to yield complementary insights regarding user-perceived ease-of-use, functionality integration, anxiety during use, and workflow impact. To illustrate its use, we applied this framework in a formative evaluation of a software called Integrated Model for Patient Care and Clinical Trials (IMPACT). We conclude that this mixed-methods evaluation framework enables an integrated assessment of user needs satisfaction and user-perceived usefulness and usability of a novel design. This evaluation framework effectively bridges the gap between co-evolving user needs and technology designs during iterative prototyping and is particularly useful when it is difficult for users to articulate their needs for technology support due to the lack of a baseline.